OrthographicNet：一种用于开放域中三维物体识别的深度迁移学习方法
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains
摘要：
如今，服务机器人越来越多地出现在我们的日常生活中。对于这种类型的机器人，开放式对象类别学习和识别是必要的，因为无论用于批量学习的训练数据有多广泛，机器人在现实世界环境中操作时都可能面临新的对象。在这项工作中，我们提出了OrthographicNet，一个基于卷积神经网络（CNN）的模型，用于开放域中的3D对象识别。特别是，OrthographicNet为给定的3D对象生成全局旋转和比例不变的表示，使机器人能够识别从不同角度看到的相同或相似的对象。实验结果表明，我们的方法产生了显着的改善，在开放式场景中的对象识别性能和可扩展性，比以前的国家的最先进的方法。此外，OrthographicNet展示了从现场很少的例子中学习新类别的能力。关于实时性能，三个真实世界的演示验证了所提出的架构的良好性能。
英文摘要：
Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a new object when operating in a real-world environment. In this work, we present OrthographicNet, a Convolutional Neural Network (CNN)-based model, for 3D object recognition in open-ended domains. In particular, OrthographicNet generates a global rotation- and scale-invariant representation for a given 3D object, enabling robots to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning object recognition performance and scalability in open-ended scenarios. Moreover, OrthographicNet demonstrates the capability of learning new categories from very few examples on-site. Regarding real-time performance, three real-world demonstrations validate the promising performance of the proposed architecture.
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